Alzheimer's disease (AD) affects a large proportion of the world's population and is primarily a disease of old age. There is little known about the early molecular pathogenesis of AD leading to the characteristic dementia. Increased knowledge of the etiologic processes leading to dementia would allow improved diagnostics and targeted therapeutics. This proposal is multifaceted and seeks to elucidate 1) what differentiates AD from normal aging processes and other dementias of old age, 2) why individuals with certain genetic backgrounds (ApoE4 alleles) are more likely to become affected (inter-individual differences), 3) and what happens at the cellular and subcellular level in response to dementia-inducing stimuli (plaques and tangles)(intra-individual differences). Taken together the expression profiling data set generated on laser capture microdissected (LCM) cells from carefully stratified patient cohorts should provide unique insight into the AD phenotype. Specific hypotheses related to energy metabolism will be validated by multiple techniques (by immunohistochemistry on independent tissues and at the functional level using neuronal cell cultures and siRNAs). These results will be made available to the general public within 6 months of generation via the most established relational database for array data, the NINDS/NIMH array consortium repository. The applicants are uniquely qualified to perform a large-scale collaborative study of this type. Working closely in collaboration with 3 Alzheimer's Disease Centers (ADCs), the PIs will have access to tissue sections from the appropriate cohorts. The use of tissue sections (as opposed to large heterogeneous pieces of brain) and LCM as the starting reagents for the expression profiling will allow generation of high quality data while not depleting the banked national resource of brains. The PI is the Chairman of a National consortium of expression profiling facilities which generate extremely high quality data on large numbers of neurological phenotypes. Integration of this data set into that repository will increase the value of the AD data set exponentially because of the increased number of comparisons which can be generated using pre-existing data. The group also has access to sophisticated validation technologies. In all, the partnership of leaders in the AD field, the national resources within the ADCs, and the genomics expertise at TGen should allow rapid progress in understanding the etiology of AD dementia.